
import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     Optional[Any])   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Dict       =   TapasConfig.from_json_file(_lowerCamelCase)
							# set absolute/relative position embeddings parameter
							__UpperCamelCase      :  int       =   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__UpperCamelCase      :  Any       =   TapasForQuestionAnswering(config=_lowerCamelCase)
							elif task == "WTQ":
														# run_task_main.py hparams
														__UpperCamelCase      :  int       =   4
														__UpperCamelCase      :  List[Any]       =   True
														# hparam_utils.py hparams
														__UpperCamelCase      :  int       =   0.6_6_4_6_9_4
														__UpperCamelCase      :  Optional[Any]       =   0.2_0_7_9_5_1
														__UpperCamelCase      :  Tuple       =   0.1_2_1_1_9_4
														__UpperCamelCase      :  Optional[Any]       =   True
														__UpperCamelCase      :  int       =   True
														__UpperCamelCase      :  Dict       =   False
														__UpperCamelCase      :  Any       =   0.0_3_5_2_5_1_3

														__UpperCamelCase      :  List[Any]       =   TapasForQuestionAnswering(config=_lowerCamelCase)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__UpperCamelCase      :  Dict       =   4
														__UpperCamelCase      :  str       =   False
														# hparam_utils.py hparams
														__UpperCamelCase      :  List[str]       =   3_6.4_5_1_9
														__UpperCamelCase      :  List[Any]       =   0.9_0_3_4_2_1
														__UpperCamelCase      :  Optional[Any]       =   2_2_2.0_8_8
														__UpperCamelCase      :  List[Any]       =   True
														__UpperCamelCase      :  Tuple       =   True
														__UpperCamelCase      :  Optional[int]       =   True
														__UpperCamelCase      :  int       =   0.7_6_3_1_4_1

														__UpperCamelCase      :  int       =   TapasForQuestionAnswering(config=_lowerCamelCase)
							elif task == "TABFACT":
														__UpperCamelCase      :  Tuple       =   TapasForSequenceClassification(config=_lowerCamelCase)
							elif task == "MLM":
														__UpperCamelCase      :  Union[str, Any]       =   TapasForMaskedLM(config=_lowerCamelCase)
							elif task == "INTERMEDIATE_PRETRAINING":
														__UpperCamelCase      :  int       =   TapasModel(config=_lowerCamelCase)
							else:
														raise ValueError(F'Task {task} not supported.')

							print(F'Building PyTorch model from configuration: {config}')
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model (weights and configuration)
							print(F'Save PyTorch model to {pytorch_dump_path}')
							model.save_pretrained(_lowerCamelCase)

							# Save tokenizer files
							print(F'Save tokenizer files to {pytorch_dump_path}')
							__UpperCamelCase      :  Union[str, Any]       =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512)
							tokenizer.save_pretrained(_lowerCamelCase)

							print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell)


if __name__ == "__main__":
		lowercase    :   Optional[int]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
		)
		parser.add_argument(
		    '--reset_position_index_per_cell',
		    default=False,
		    action='store_true',
		    help='Whether to use relative position embeddings or not. Defaults to True.',
		)
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--tapas_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained TAPAS model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Tuple						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)